摘要： 用最大间距准则(Maximum Margin Criterion,MMC)算法进行特征提取时,提取的是全局的特征,对局部的特征不能有效地抽取。因此,对MMC算法进行改进,提出一种基于分块MMC(Modular Maximum Margin Criterion,MMMC)的人脸识别方法。首先对图像矩阵进行分块,然后对分块后的矩阵进行MMC特征抽取,对每一子块抽取的特征进行整体融合,最后采用最近邻判决准则进行分类识别。在ORL、Yale人脸图像库进行的实验结果表明,新算法相比于MMC算法有更好的识别性能。
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